Abstract
The process of collecting and annotating training data may introduce distribution artifacts which may limit the ability of models to learn correct generalization behavior. We identify failure modes of SOTA relation extraction (RE) models trained on TACRED, which we attribute to limitations in the data annotation process. We collect and annotate a challenge-set we call Challenging RE (CRE), based on naturally occurring corpus examples, to benchmark this behavior. Our experiments with four state-of-the-art RE models show that they have indeed adopted shallow heuristics that do not generalize to the challenge-set data. Further, we find that alternative question answering modeling performs significantly better than the SOTA models on the challenge-set, despite worse overall TACRED performance. By adding some of the challenge data as training examples, the performance of the model improves. Finally, we provide concrete suggestion on how to improve RE data collection to alleviate this behavior.- Anthology ID:
- 2020.emnlp-main.302
- Volume:
- Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Bonnie Webber, Trevor Cohn, Yulan He, Yang Liu
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3702–3710
- Language:
- URL:
- https://aclanthology.org/2020.emnlp-main.302
- DOI:
- 10.18653/v1/2020.emnlp-main.302
- Cite (ACL):
- Shachar Rosenman, Alon Jacovi, and Yoav Goldberg. 2020. Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 3702–3710, Online. Association for Computational Linguistics.
- Cite (Informal):
- Exposing Shallow Heuristics of Relation Extraction Models with Challenge Data (Rosenman et al., EMNLP 2020)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2020.emnlp-main.302.pdf
- Code
- shacharosn/CRE